Magnetic resonance imaging (MRI) is a medical imaging modality that can create images of the inside of a human body without using x-rays or other ionizing radiation. MRI uses a powerful magnet to create a strong, uniform, static magnetic field (i.e., the “main magnetic field”). When a human body, or part of a human body, is placed in the main magnetic field, the nuclear spins that are associated with the hydrogen nuclei in tissue water become polarized. This means that the magnetic moments that are associated with these spins become preferentially aligned along the direction of the main magnetic field, resulting in a small net tissue magnetization along that axis (the “z axis”, by convention). A MRI system also comprises components called gradient coils that produce smaller amplitude, spatially varying magnetic fields when current is applied to them. Typically, gradient coils are designed to produce a magnetic field component that is aligned along the z axis, and that varies linearly in amplitude with position along one of the x, y or z axes. The effect of a gradient coil is to create a small ramp on the magnetic field strength, and concomitantly on the resonance frequency of the nuclear spins, along a single axis. Three gradient coils with orthogonal axes are used to “spatially encode” the MR signal by creating a signature resonance frequency at each location in the body. Radio frequency (RF) coils are used to create pulses of RF energy at or near the resonance frequency of the hydrogen nuclei. These coils are used to add energy to the nuclear spin system in a controlled fashion. As the nuclear spins then relax back to their rest energy state, they give up energy in the form of an RF signal. Such signals are detected by the MRI system and are transformed into an image using a computer and known reconstruction algorithms.
MR images may be created by using a two-dimensional (2D) or a three-dimensional (3D) acquisition strategy, the most common of which are rectilinear sampling strategies that fill a 2D or 3D Cartesian grid with Fourier reciprocal space (i.e., “k-space”) data. The data may be collected with Nyquist frequency sampling to provide unique location encoding of the MRI signals and thereby prevent aliasing in the reconstructed images. Two-dimensional data is spatially encoded using phase-encoding along the y direction and frequency encoding along the x direction. Three-dimensional data is spatially encoded using phase-encoding along two perpendicular spatial directions (the y and z directions) and frequency-encoding along the third (the x direction). Usually, the secondary phase-encoding is referred to as “slice-encoding,” to distinguish it from the primary phase-encoding. The resultant raw data fills a 2D or 3D k-space matrix which is then “reconstructed” into images using Fourier transformation techniques.
To reduce MRI data acquisition time, “parallel imaging” (also known as “partially parallel imaging”) techniques may be used in which k-space is under-sampled (i.e., the Nyquist criteria is not met) and the signals from multiple receiver coils are combined to provide aliasing-free images. Parallel imaging techniques use spatial sensitivity profiles of the individual receiver coils in addition to traditional gradient spatial encoding techniques to recover the MRI signals from individual voxels in a source volume of interest. Parallel imaging has been shown to be successful in reducing scan time and has also found application in reducing image blurring and geometric distortions in pulse sequences using long echo trains.
Two families of parallel imaging techniques are known in the art for generating images from incompletely sampled data, based either on the SENSE technique (SENSitivity Encoding) or on the SMASH technique (SiMultaneous Acquisition of Spatial Harmonics). The SENSE-based techniques separately transform the undersampled individual receiver coil k-space data sets into image-space, resulting in spatially aliased images. The aliased images are then combined using weights constructed from measured spatial sensitivity profiles from the individual receiver coils to give a final image with the aliasing artifacts removed. The first SMASH-based techniques developed also used measured spatial sensitivity profiles. These measured spatial sensitivity profiles were used to determine mathematical relationships between neighboring k-space lines in order to synthesize unacquired k-space lines from acquired lines.
More recently, autocalibrated imaging (ACI) techniques based on SMASH, such as AUTO-SMASH, VD-AUTO-SMASH, and GRAPPA, have been developed that do not require a separate acquisition of data to characterize the spatial sensitivity profiles of the individual receiver coils. Instead, a small region in k-space is acquired with full Nyquist sampling as part of an overall undersampled acquisition. The fully sampled region in k-space is used to determine reconstruction weights or coefficients that allow the unacquired data in k-space to be synthesized from the acquired data. The extra data obtained in the fully sampled region in k-space is referred to as autocalibrating signals (ACS).
Many imaging applications require the acquisition of images of the same volume of interest at multiple time points. Such applications may be known as “multi-point studies.” For autocalibrating parallel imaging (API) techniques, ACS data from each time point are typically acquired at the same points of k-space, such that consistent SNR and k-space weightings may be achieved for all time points. Increasing the amount of ACS data used in reconstructing an image improves the quality of the reconstruction, however, it also increases the acquisition time. Accordingly, it would be desirable to provide a method for combining k-space data (e.g., data from the fully sampled region of k-space including ACS data) from multiple time points for reconstructing each image in the multi-point study.